A Procedural Definition of Multi-word Lexical Units
Bibliographic record
Abstract
Multi-word expressions evade a closed definition. Linguists and computational linguists rely on intuition or build lists of MWE types; while practical, that is scientifically and aesthetically unsatisfying. Without presuming to solve a daunting theoretical problem, we propose a decision procedure which steers a lexicographer toward acceptance or rejection of an N-gram as a lexical unit: a decision tree classifies N-grams as MWE or not MWE. It will succeed if it agrees with the native speakers’ judgment. We need a small, linguistically credible set of features, to contend with the multiplicity of adequate trees. Decision tree induction works with a fixed set of annotated classification examples, but the lexical material for MWE recognition is too large to make annotation feasible. We rely on small-scale statistically significant sampling, and on intuition. Of a few decision trees produced by informed trial and error, we select one we consider best in our circumstances. That tree, deployed in a large-scale wordnet construction project, allowed us to gather dependable statistics on its usefulness in lexicographers’ work. Our goal: systematic expansion of a wordnet by tens of thousands of MWEs in a manner as free of personal biases as possible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".